In the medical industry, there is often a need for a laboratory technician, e.g., a cytotechnologist, to review a cytological specimen for the presence of specified cell types. For example, there is presently a need to review a cervico-vaginal Papanicolaou (Pap) smear slides for the presence of malignant or pre-malignant cells. Since its introduction over fifty years ago, Pap smears have been a powerful tool for detecting cancerous and precancerous cervical lesions. During that time, the Pap smear has been credited with reducing mortality from cervical cancer by as much as 70%. This once precipitous drop in the death rate has slowed however, and the mortality rate in the United States for this preventable disease has remained virtually constant, at about 5,000 per year since the mid-eighties. Therefore, about one-third of the 15,000 women diagnosed with cervical cancer annually still die, because the cancer was detected too late. A further cause for concern is National Cancer Institute data that shows an annual 3% increase in the incidence of invasive cervical cancer in white women under 50 years of age since 1986.
A number of factors may be contributing to this current threshold, not the least of which is the fact that many women, particularly in high risk populations, are still not participating in routine cervical cancer screening. Another contributing factor that has received much attention is the limitation of the traditional Pap smear method itself.
The reliability and efficacy of a cervical screening method is measured by its ability to diagnose precancerous lesions (sensitivity) while at the same time avoiding false positive diagnosis (specificity). In turn, these criteria are dependent on the accuracy of the cytological interpretation. The conventional Pap smear has false negative rates ranging from 10-50%. This is due in large part to the vast number of cells and objects (typically as many as 100,000 to 200,000) that must be reviewed by a technician to determine the possible existence of a small number of malignant or pre-malignant cells. Thus, Pap smear tests, as well as other tests requiring detailed review of biological material, have suffered from a high false negative rate due to fatigue imposed on the technician.
To facilitate this review process, automated systems have been developed by various companies to focus the technician's attention on the most pertinent cells, with a potential to discard the remaining cells from further review. A typical automated system includes an imager and an automated optical microscope. Briefly, the imager can be operated to provide a series of numerous images of a cytological specimen slide, referred to as image frames, each depicting a different portion of the slide. The imager then processes these image frames to determine the most pertinent biological objects for review on the slide, and their locations (x-y coordinates) on the slide. This information is then passed on to the microscope, which, based on the x-y coordinates received from the imager, automatically or semi-automatically displays the biological objects for review by the technician. The technician can then mark any objects on the slide that he or she believes require further review by a pathologist, for example, any objects having attributes consistent with malignant or pre-malignant cells. In general, this automated procedure has proved to be successful, since the technician's attention is focused on a limited number of objects, obviating the need for the technician to review the vast number of objects (biological or not) on the specimen.
In typical automated imaging/review systems, such as those used in cytological applications, it is desirable to capture an entire object of interest of a specimen within a single image data frame, so that an object or feature presented on the slide or other carrier, can be classified or otherwise processed in an accurate manner. That is, because the image frames are processed on a frame-by-frame basis, an object that only has a portion represented in an image frame might be ignored or misclassified when that image frame is processed.
One way to do address this concern is to configure the image frames in an overlapping manner. For example, FIG. 1 illustrates a subset of image frames IF that depict objects O carried by a slide (not shown). While it can be appreciated that many image frames are typically used to capture many more objects in standard imaging systems, for purposes of brevity and clarity, only three image frames IF1, IF2, and IF3 and three objects O1, O2, and O3 are illustrated in FIG. 1. The imager is configured, such that the image frames have an overlap factor in both the horizontal and vertical directions in an attempt to ensure that objects that are touching or split across the border of an image frame will be entirely represented in another image frame. For example, object O1 is illustrated as being entirely contained in either of image frames IF1 and IF2. While object O2 is illustrated as crossing the border of image frame IF1, it is entirely contained within image frame IF2. Thus, in these cases, objects O1 and O2 should be accurately classified or otherwise processed.
However, because the image frame processing efficiency decreases as the overlap factor increases, the amount that any particular image frame overlaps another is limited. As such, it is possible that an object may not be entirely contained within a single frame even if an overlapping image frame configuration is employed. For example, object O3 illustrated in FIG. 1 is so large that is crosses both borders of adjacent image frames IF2 and IF3, and thus, will not be entirely contained within a single image frame. Thus, it is quite possible that object O3 will either be ignored or inaccurately represented when either of the image frames IF2 and IF3 are processed.
In the case where the specimen is to be classified based on the consideration of the objects contained within the specimen, the fact that a particular object is ignored or inaccurately represented may not have a significant negative impact on the classification of such specimen when there are many other diagnostically equivalent objects similarly situated to the ignored or inaccurately represented object, since it is very likely that at least one of these objects will be entirely located within an image frame, and thus accurately processed. However, when there are very few objects or no other object similarly situated to the ignored or inaccurately represented object, it is quite possible that none of these objects will be entirely located within an image frame, and either ignored or inaccurately processed for this reason or other reasons. As such, it may be important to accurately process an object that is not entirely contained within a single image frame.
There, thus, remains a need to improve automated imaging systems, such as those used to image and process cytological specimens.